想请教关于人脸识别 LBP子模式的代码，不知道在openmv上该怎么实现，因为运行例程里的LBP算法，感觉效果并不好，看论文有看到LBP子模式啊，金字塔方式的多尺度LBP啊之类的，想请教下各位大神该怎么做。

import sensor,image,time



sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)
sensor.set_gainceiling(16)
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time = 2000) # Let new settings take affect.

# Load up a face detection HaarCascade. This is object that your OpenMV Cam
# can use to detect faces using the find_features() method below. Your OpenMV
# Cam has fontalface HaarCascade built-in. By default, all the stages of the
# HaarCascade are loaded. However, You can adjust the number of stages to speed
# up processing at the expense of accuracy. The frontalface HaarCascade has 25
# stages.
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)
while(True):
    img = sensor.snapshot()
        # Threshold can be between 0.0 and 1.0. A higher threshold results in a
        # higher detection rate with more false positives. The scale value
        # controls the matching scale allowing you to detect smaller faces.
    faces = img.find_features(face_cascade, threshold=0.75, scale_factor=1.35)


    for r in faces:
        img.draw_rectangle(r)
    

尝试过更改stage,threshold和scale_factor，但无法像视频教程14那样持续且准确地框出人脸，stage偏大时无法显示矩形框，想请问是openmv自带模型的问题？摄像头的问题？计算机配置问题？还是其他问题？
print(face_cascade)在串行终端显示为"width":24, "height":24, "n_stages":25, "n_features":2913, "n_rectangles":6383

问题解决了，非常感谢！

@kidswong999 谢谢指导！前面试了一下，把电脑关机后我先插了SD卡，然后开电脑，最后连硬件，但问题依然存在，请问我的顺序正确吗？

import sensor, image, pyb

RED_LED_PIN = 1
BLUE_LED_PIN = 3

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time = 2000) # Let new settings take affect.

# Load up a face detection HaarCascade. This is object that your OpenMV Cam
# can use to detect faces using the find_features() method below. Your OpenMV
# Cam has fontalface HaarCascade built-in. By default, all the stages of the
# HaarCascade are loaded. However, You can adjust the number of stages to speed
# up processing at the expense of accuracy. The frontalface HaarCascade has 25
# stages.
face_cascade = image.HaarCascade("frontalface", stages=25)

while(True):

    pyb.LED(RED_LED_PIN).on()
    print("About to start detecting faces...")
    sensor.skip_frames(time = 2000) # Give the user time to get ready.

    pyb.LED(RED_LED_PIN).off()
    print("Now detecting faces!")
    pyb.LED(BLUE_LED_PIN).on()

    diff = 10 # We'll say we detected a face after 10 frames.
    while(diff):
        img = sensor.snapshot()
        # Threshold can be between 0.0 and 1.0. A higher threshold results in a
        # higher detection rate with more false positives. The scale value
        # controls the matching scale allowing you to detect smaller faces.
        faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)

        if faces:
            diff -= 1
            for r in faces:
                img.draw_rectangle(r)

    pyb.LED(BLUE_LED_PIN).off()
    print("Face detected! Saving image...")
    sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic.

前面运行正常，能框出人脸，最后一步报错，报错内容见标题

补充：代码为snapshot的例程

SD卡已插入。
请问是什么原因造成的？